Hyperspectral Anomaly Detection Based on Empirical Mode Decomposition and Local Weighted Contrast

Existing hyperspectral anomaly detection methods leverage the spectral and spatial information in the hyperspectral image (HSI) to detect the anomalies. However, the performance of these methods is limited due to the spectral variability of the materials. To address this issue, a hyperspectral anomaly detection method, based on empirical mode decomposition (EMD) and local weighted contrast (HELWC), is proposed. First, a novel spectral decomposition method based on EMD is adopted to reduce the spectral noise, and estimate the spectral reference of the material. Second, spectral Jensen-Shannon (JS) divergence is utilized to describe the distances between different spectra. Subsequently, a local weighted contrast (LWC) estimation method is proposed to calculate the contrast score. Finally, the contrast score is multiplied with the test pixel to obtain the final detection result. Experiments are conducted on four real-world datasets using multiple metrics for comparing the proposed and the benchmark approaches. The results demonstrate that the proposed method effectively reduces spectral noise and suppresses background. The average $\text {AUC}_{({D}, {F})}$ of the proposed method on the four datasets is 0.9965, which is 0.0014 higher than the second ranked baseline method. Overall, the proposed algorithm outperforms the nine state-of-the-art methods in both subjective and objective evaluations.